As the U.S. population ages, there is growing concern about the loss of mental acuity associated with aging. Even small deficits are considered intermediates between normal cognitive attrition and severe conditions, such as Alzheimer?s disease. Mild cognitive impairment, which affects ~10% of those ?65 years, has a 56% conversion rate to dementia over 4 years. Because of dementia?s strong age dependence, merely delaying its onset could have a dramatic impact. A 5-year delay in the onset of Alzheimer?s disease would lead to ~4 million fewer cases in the U.S. The lack of biomarkers that reflect adverse exposures and preclinical effects dramatically limits opportunities for effective targeted prevention. Environmental exposures that augment systemic inflammation, such as ambient air pollution and metals, have been shown to hasten cognitive aging by as much as 5 years. We also recently showed that long term exposure to air pollution is associated with Alzheimer's disease incidence. Our goal is to identify new biomarkers that reflect environmental influences and predict the risk of impaired cognition. We will leverage experimental and clinical evidence on extracellular vesicles (EVs)?i.e., tiny membrane-bound vesicles actively released by tissue cells into the bloodstream?and of their bioactive cargo of micro (miRNAs) and long (lncRNAs) noncoding RNAs, which can signal inflammatory responses via their potent capacity for gene regulation. Animal and human studies have shown that environmental exposures induce the release of pro-inflammatory EVs into the bloodstream. Recent clinical data also show that changes in blood EV biomarkers precede and predict cognitive impairment. In particular, a small, but highly suggestive, case-control study identified EV-miRNAs in patients with Alzheimer?s disease, relative to controls, which were used?by means of a machine learning model (which we will also employ)?to identify early preclinical risk of Alzheimer?s disease. In the proposed studies, we will leverage the resources of well-phenotyped longitudinal cohorts. First, in the Normative Aging Study, we will access ready-to-use longitudinal collections of biospecimens, exposure data, and cognitive function measurements from four serial visits over 12 years of follow up. We hypothesize that environmental exposures to air pollution and metals, individually and as mixtures, will be associated with significantly higher numbers of EVs and differential EV size (Aim 1). We further hypothesize that the levels of and longitudinal changes in EV-encapsulated miRNAs and lncRNAs reflect current and past environmental exposures and predict subsequent cognitive decline (Aim 2). Finally, we will use machine learning and mediation analysis to determine the roles of circulating EVs and their noncoding RNA cargo on the biological pathways linking environmental exposures to cognitive decline (Aim 3). We will validate all findings in the KORA cohort, which has similar design and data. Our work may yield a model for other potentially modifiable risk factors for impaired cognition and Alzheimer?s disease, as well as for additional age-related diseases.